Fully automated kidney image biomarker prediction in ultrasound scans using Fast-Unet+.

Any kidney dimension and volume variation can be a remarkable indicator of kidney disorders. Precise kidney segmentation in standard planes plays an undeniable role in predicting kidney size and volume. On the other hand, ultrasound is the modality of choice in diagnostic procedures.

A novel deep learning model for breast lesion classification using ultrasound Images: A multicenter data evaluation.

Purpose: Breast cancer is one of the major reasons of death due to cancer in women. Early diagnosis is the most critical key for disease screening, control, and reducing mortality. A robust diagnosis relies on the correct classification of breast lesions.

Clinical target segmentation using a novel deep neural network: double attention Res-U-Net.

We introduced Double Attention Res-U-Net architecture to address medical image segmentation problem in different medical imaging system. Accurate medical image segmentation suffers from some challenges including, difficulty of different interest object modeling, presence of noise, and signal dropout throughout the measurement. The base line image segmentation approaches are not sufficient for complex target segmentation throughout the various medical image types.

Automatic cardiac evaluations using a deep video object segmentation network.

Background: Accurate cardiac volume and function assessment have valuable and significant diagnostic implications for patients suffering from ventricular dysfunction and cardiovascular disease. This study has focused on finding a reliable assistant to help physicians have more reliable and accurate cardiac measurements using a deep neural network. EchoRCNN is a semi-automated neural network for echocardiography sequence segmentation using a combination of mask region-based convolutional neural network image segmentation structure with reference-guided mask propagation video object segmentation network.

Fast and Accurate U-Net Model for Fetal Ultrasound Image Segmentation.

U-Net based algorithms, due to their complex computations, include limitations when they are used in clinical devices. In this paper, we addressed this problem through a novel U-Net based architecture that called fast and accurate U-Net for medical image segmentation task. The proposed fast and accurate U-Net model contains four tuned 2D-convolutional, 2D-transposed convolutional, and batch normalization layers as its main layers.

Automatic fetal biometry prediction using a novel deep convolutional network architecture.

Purpose: Fetal biometric measurements face a number of challenges, including the presence of speckle, limited soft-tissue contrast and difficulties in the presence of low amniotic fluid. This work proposes a convolutional neural network for automatic segmentation and measurement of fetal biometric parameters, including biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), and femur length (FL) from ultrasound images that relies on the attention gates incorporated into the multi-feature pyramid Unet (MFP-Unet) network.

Methods: The proposed approach, referred to as Attention MFP-Unet, learns to extract/detect salient regions automatically to be treated as the object of interest via the attention gates.

Organ donation workshop - a survey on nurses' knowledge and attitudes toward organ and tissue donation in Iran.

Purpose: Despite increasing transplantation in Iran, organ shortage and long waiting lists remain major problems in the country. Many publications have demonstrated that the willingness of healthcare professionals to participate in the donation process can improve the donation rate. Since nurses are usually the first people among the healthcare staff to recognize a patient as a potential donor, they have an important role in the procurement of organ and tissue from cadaveric donors.